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PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store
https://ipsj.ixsq.nii.ac.jp/records/185893
https://ipsj.ixsq.nii.ac.jp/records/185893122a96c7-4117-484c-84ca-9715f41d1fdf
名前 / ファイル | ライセンス | アクション |
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Copyright (c) 2018 by the Information Processing Society of Japan
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オープンアクセス |
Item type | Journal(1) | |||||||||||||||
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公開日 | 2018-02-15 | |||||||||||||||
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タイトル | PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store | |||||||||||||||
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言語 | en | |||||||||||||||
タイトル | PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store | |||||||||||||||
言語 | ||||||||||||||||
言語 | eng | |||||||||||||||
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主題Scheme | Other | |||||||||||||||
主題 | [一般論文(推薦論文)] mobile app store, promotional attack, machine learning | |||||||||||||||
資源タイプ | ||||||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
資源タイプ | journal article | |||||||||||||||
著者所属 | ||||||||||||||||
Waseda University | ||||||||||||||||
著者所属 | ||||||||||||||||
The Hong Kong Polytechnic University | ||||||||||||||||
著者所属 | ||||||||||||||||
NTT Secure Platform Laboratories | ||||||||||||||||
著者所属 | ||||||||||||||||
NTT Secure Platform Laboratories | ||||||||||||||||
著者所属 | ||||||||||||||||
Waseda University | ||||||||||||||||
著者所属(英) | ||||||||||||||||
en | ||||||||||||||||
Waseda University | ||||||||||||||||
著者所属(英) | ||||||||||||||||
en | ||||||||||||||||
The Hong Kong Polytechnic University | ||||||||||||||||
著者所属(英) | ||||||||||||||||
en | ||||||||||||||||
NTT Secure Platform Laboratories | ||||||||||||||||
著者所属(英) | ||||||||||||||||
en | ||||||||||||||||
NTT Secure Platform Laboratories | ||||||||||||||||
著者所属(英) | ||||||||||||||||
en | ||||||||||||||||
Waseda University | ||||||||||||||||
著者名 |
Bo, Sun
× Bo, Sun
× Xiapu, Luo
× Mitsuaki, Akiyama
× Takuya, Watanabe
× Tatsuya, Mori
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著者名(英) |
Bo, Sun
× Bo, Sun
× Xiapu, Luo
× Mitsuaki, Akiyama
× Takuya, Watanabe
× Tatsuya, Mori
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論文抄録 | ||||||||||||||||
内容記述タイプ | Other | |||||||||||||||
内容記述 | Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.26(2018) (online) DOI http://dx.doi.org/10.2197/ipsjjip.26.212 ------------------------------ |
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論文抄録(英) | ||||||||||||||||
内容記述タイプ | Other | |||||||||||||||
内容記述 | Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.26(2018) (online) DOI http://dx.doi.org/10.2197/ipsjjip.26.212 ------------------------------ |
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書誌レコードID | ||||||||||||||||
収録物識別子タイプ | NCID | |||||||||||||||
収録物識別子 | AN00116647 | |||||||||||||||
書誌情報 |
情報処理学会論文誌 巻 59, 号 2, 発行日 2018-02-15 |
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ISSN | ||||||||||||||||
収録物識別子タイプ | ISSN | |||||||||||||||
収録物識別子 | 1882-7764 |
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Cite as
Bo, Sun, Xiapu, Luo, Mitsuaki, Akiyama, Takuya, Watanabe, Tatsuya, Mori, 2018.
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